6 research outputs found
Gaussian-based Probabilistic Deep Supervision Network for Noise-Resistant QoS Prediction
Quality of Service (QoS) prediction is an essential task in recommendation
systems, where accurately predicting unknown QoS values can improve user
satisfaction. However, existing QoS prediction techniques may perform poorly in
the presence of noise data, such as fake location information or virtual
gateways. In this paper, we propose the Probabilistic Deep Supervision Network
(PDS-Net), a novel framework for QoS prediction that addresses this issue.
PDS-Net utilizes a Gaussian-based probabilistic space to supervise intermediate
layers and learns probability spaces for both known features and true labels.
Moreover, PDS-Net employs a condition-based multitasking loss function to
identify objects with noise data and applies supervision directly to deep
features sampled from the probability space by optimizing the Kullback-Leibler
distance between the probability space of these objects and the real-label
probability space. Thus, PDS-Net effectively reduces errors resulting from the
propagation of corrupted data, leading to more accurate QoS predictions.
Experimental evaluations on two real-world QoS datasets demonstrate that the
proposed PDS-Net outperforms state-of-the-art baselines, validating the
effectiveness of our approach
Recent Advances in Social Data and Artificial Intelligence 2019
The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace
Reports to the President
A compilation of annual reports for the 1999-2000 academic year, including a report from the President of the Massachusetts Institute of Technology, as well as reports from the academic and administrative units of the Institute. The reports outline the year's goals, accomplishments, honors and awards, and future plans